Generative modelling of convergence maps based on predicted one-point statistics

Weak gravitational lensing is a key cosmological probe for current and future large-scale surveys. While power spectra are commonly used for analyses, they fail to capture non-Gaussian information from non-linear structure formation, which necessitates higher-order statistics and methods for an efficient map generation. We develop an emulator that generates accurate convergence (ąppa) maps directly from an input power spectrum and wavelet ell_1-norm without relying on computationally intensive simulations. We used either numerical or theoretical predictions to construct ąppa maps by iteratively adjusting the wavelet coefficients to match the marginal distributions of the target and their inter-scale dependences by incorporating higher-order statistical information. The resulting ąppa maps accurately reproduce the input power spectrum, and their higher-order statistical properties are consistent with the input predictions. They thus provide an efficient tool for weak-lensing analyses.